CN107748933A - Meteorological element message data error correcting method, mist, sunrise, sea of clouds, rime Forecasting Methodology - Google Patents
Meteorological element message data error correcting method, mist, sunrise, sea of clouds, rime Forecasting Methodology Download PDFInfo
- Publication number
- CN107748933A CN107748933A CN201710996151.0A CN201710996151A CN107748933A CN 107748933 A CN107748933 A CN 107748933A CN 201710996151 A CN201710996151 A CN 201710996151A CN 107748933 A CN107748933 A CN 107748933A
- Authority
- CN
- China
- Prior art keywords
- landscape
- probability
- day
- rime
- happening
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
Abstract
Exist for existing meteorological element Forecasting Methodology and deficiency is considered to local environment particularity and the numerical value of prediction the defects of error is more easily occurred, the invention provides a kind of meteorological element message data error correcting method, for playing report day amendment meteorological observatory to the public meteorological telegraphic messages data A issuedyObtain predicting day meteorological element correction value A.The present invention also provides by a kind of mist Occurrence forecast method of the meteorological element message data error correcting method realization mist probability of happening secondary during for reporting day measuring and calculating prediction daily forecast, a kind of sunrise landscape forecast method is used to the probability of sunrise landscape occur in measuring and calculating prediction day report day, a kind of sea of clouds landscape forecast method is used for the probability of time generation sea of clouds landscape when daily forecast is predicted in a report day measuring and calculating, a kind of rime landscape forecast method is used to the probability of rime landscape occur in measuring and calculating prediction day report day, and Mount Emei scenic spot mist Occurrence forecast method, sunrise landscape forecast method, sea of clouds landscape forecast method, rime landscape forecast method.
Description
Technical field
The present invention relates to a kind of method that message data to meteorological element carries out error correction, and mist, sunrise, cloud
Four kinds of sea, rime meteorological diversity scenery Forecasting Methodologies, belong to meteorologic survey observation, forecast field.
Background technology
Meteorological element is to show certain location and particular moment atmospheric physics state, physical phenomenon, the items of physical process
Physical quantity.Mainly there are temperature, air pressure, wind, humidity, cloud, precipitation and various weather phenomena.
Weather forecast (survey) is that the state of following a certain place earth atmosphere is predicted using modern science and technology.
Current weather forecast issues predicted value with message data, and the acquisition of predicted value is the knot based on " Numerical Weather pattern " prediction
Fruit.Numerical weather prediction is carried out, needs the airspace above earth's surface being divided into many sub-boxes first, and utilize various
Observation instrument, obtains the distributed data of air three dimensions, then analyzes and calculate the various atmosphere data in each lattice, then
By these data input supercomputers, extremely complex fortune is carried out according to the air equation set by computer
Calculate, calculate following possible Changes in weather.In order to reduce calculated load in practice, Meteorological Unit can be according to distance, to the earth
On diverse location use different lattice modes.The basic ideas of present technology are (temperature, wet using substantial amounts of data are collected
Degree, wind direction and wind speed, air pressure etc.), then using determining following air change to the understanding (meteorology) of Atmospheric processes at present
Change.Because the chaotic and existing science and technology of Atmospheric processes does not fully understand finally Atmospheric processes, therefore weather is pre-
Report always has certain error.
According to the weather prognosis principle of prior art, due to environmental condition (such as a certain city size of population in some areas
Change, the influence of this area's vegetation coverage, each weather station is to this area's space length and this area mountain range, lake
Etc. factor) the meteorological element value in some areas is directly influenced, thus it is (such as all kinds of in the higher area of local environment particularity
Natural scenic spot), more easily there is error in the numerical value of weather prognosis.The country substantially achieves each city above county level at present
City has automatic meteorological observation station, and each weather station is responsible for collecting, uploading the meteorological element data of this area, but
Then lack the automatic Observation station that function aligns complete in scenic spot.How the weather forecast numerical error of public report is repaiied
Just, natural scenic spot is enable to establish forecast model using meteorological broadcast numerical value, compared with each meteorological element of Accurate Prediction and all kinds of gas
As landscape (such as sunrise, sea of clouds, rime, glaze), both with scientific research value, there is actual application value again.
The content of the invention
The purpose of the present invention is aiming at the deficiencies in the prior art, there is provided a kind of meteorological element message data error correction side
Method, this method can carry out error correction to the predicted value of meteorological element message data, and the predicted value for improving regional area is accurate
Degree.On this basis, the present invention is provided for four kinds of mist, sunrise, sea of clouds, rime meteorological diversity scenery Forecasting Methodologies, specific aim solution again
The problem of certainly improving the meteorological diversity scenery Occurrence forecast degree of accuracy of some areas (such as natural scenic spots).
To achieve the above object, present invention firstly provides a kind of meteorological element message data error correcting method, its technology
Scheme is as follows：
A kind of meteorological element message data error correcting method, for playing report day amendment meteorological observatory to the public meteorology issued
Message data AyObtain predicting day meteorological element correction value A, it is characterised in that：Prediction calculates day meteorological element correction value A according to formula 1
It is determined that：
A=Ay- Δ A formulas 1
In formula, A --- prediction day meteorological element correction value,
Ay--- report day meteorological element message data is played,
Δ A --- error correction values, calculate and determine according to formula 2；
In formula, i --- it is used for the number of days of error correction reporting 24h a few days ago from forward, is determined according to history message data；
ΔAi--- in the number of days of error correction, each meteorological element message data AyWith corresponding live observation Az
Error.
Above-mentioned meteorological element message data error correcting method is in the prediction day gas for playing report day to meteorological observatory to public affairs issue
As the meteorological element predicted value progress error correction of key element message data, and obtain the method for predicting day meteorological element correction value.
Prediction day meteorological element correction value A has been report day meteorological element message data AyAnd error correction values Δ A difference, the key of method
It is to calculate to determine error correction values Δ A with corresponding live observation data using history message data.Δ A is in history message number
On the basis of Value Data is observed with fact, count empirically determined using historical data.
In the above method, meteorological element can be temperature, relative humidity, low cloud cover or total amount of cloud.Repaiied for calculation error
Positive value delta A historical data number of days has generally comprised report 5 days a few days ago.
Based on above-mentioned meteorological element message data error correcting method, occur the present invention further provides a kind of mist pre-
Survey method, its technical scheme are as follows：
A kind of mist Occurrence forecast method realized using above-mentioned meteorological element message data error correcting method, for rising
Day measuring and calculating is reported to predict mist probability of happening F secondary during daily forecast, it is characterised in that：Implement according to following steps：
Step S1, relative humidity correction value RH secondary during prediction daily forecast is calculated；
Using described in claim 1 meteorological element message data error correcting method calculate prediction daily forecast when time it is relative
Humidity correction value RH, the meteorological element are relative humidity, the meteorological telegraphic messages data AyIt is that meteorological observatory issues day in a report
Predict message data secondary during daily forecast, the i=5；
Step S2, statistics minimum value RH is determinedzmin
Time there is mist that day occurs in the relative humidity fact observation given the correct time in advance time when in nearly 5 years of point moon statistics per daily forecast
RHz, obtain the monthly pre- time statistics minimum value RH that gives the correct timezmin；
Step S3, determine that statistical probability F ' occurs for mist
Secondary relative humidity fact observation RH when dividing moon statistics nearly 5 years per daily forecastzReach RHzminNumber of days；According to
RHzDivision statistics section, it is determined that monthly each RHzThe number of days for having mist to occur in section, statistics occurs for time mist general when obtaining every lunate tail
Rate F '；
Step S4, live observation minimum value RH ' is determinedzmin
Having inquired about in report 30 days a few days ago time has mist that day occurs in the relative humidity live observation given the correct time in advance time during per daily forecast
Value RHzMinimum value RH 'zmin, record daily minimum value；The minimum value of nearly 5 times is recorded if intrinsic fog on the 30th occurs to be more than 5 times；
Step S5, prediction day mist probability of happening F is judged according to following manner
If the pre- time relative humidity correction value RH ＜ statistics minimum values RH that gives the correct time obtained by step S1zminMinimum value is observed with fact
RH′zminSmaller, judge to predict day in the mist probability of happening F=0% to give the correct time in advance time；
If the pre- time relative humidity correction value RH >=statistics minimum value RH that gives the correct time obtained by step S1zminMinimum value is observed with fact
RH′zminSmaller, judge to predict that day in the mist probability of happening F to give the correct time in advance time is moon RH where prediction dayzIt is corresponding to count section
Mist statistical probability F ' occurs.
Above-mentioned mist Occurrence forecast method, it is the method for the mist probability of happening secondary when playing the measuring and calculating prediction daily forecast of report day.Side
The general principle of method is：One, mist are due to the larger formation of relative humidity.This method determines to need to predict that mist occurs generally first
The pre- of rate is given the correct time time (needing the mist probability of happening of which time to predicting day to enter time prediction), then uses the present invention is meteorological will
Plain message data error correcting method is corrected to playing the prediction day relative humidity message data announced report day, is obtained relatively wet
Spend bases of the correction value RH as Accurate Prediction.Secondly, using statistics minimum value RHzminWith fact observation minimum value RH 'zminTwo
Person is collectively as determination prediction day in the threshold value of secondary mist probability of happening zero probability of giving the correct time in advance.It is using the reason for two threshold values
Relative humidity is to determine an important meteorological factor of mist, and relative humidity is bigger, and easier formation mist, vice versa.Therefore, will
The historical statistics minimum value of relative humidity and 5 days live critical conditions for observing minimum values as generation mist for having mist recently.Its
3rd, after zero probability is excluded, in the case of mist probability of happening is more than zero, according to by the live observation primary system of relative humidity history
Count the mist drawn and mist probability of happening F secondary during statistical probability F ' determinations prediction daily forecast occurs.Reason is to draw relative humidity
It is divided into a series of sections, by history mass data, counts the probability that mist occurs for each relative humidity section of each month.It is based on
The probability of mist occurs for the statistical law, estimation prediction day.
It is visible according to the technical concept of above-mentioned mist Occurrence forecast method, repaiied using meteorological element message data error of the present invention
The purpose that is modified of relative humidity when correction method is to prediction daily forecast time be to make message data with live value more closely,
Thus precision of prediction is improved.Thus, mist Occurrence forecast method provided by the invention be not modified to relative humidity predicted value
In the case of, it is still a complete technical scheme.Further, error between the predicted value for relative humidity and live value
Originally less area, mist probability of happening can be predicted directly based on predicted value.That is, for above-mentioned mist Occurrence forecast method,
Relative humidity predicted value secondary during prediction daily forecast is directly obtained in step sl, in step s 5 using relative humidity predicted value
With counting minimum value RHzmin, live observation minimum value RH 'zminIt is compared.
Based on above-mentioned meteorological element message data error correcting method, mist Occurrence forecast method, the present invention provides one
Kind sunrise landscape forecast method, its technical scheme are as follows：
A kind of sunrise realized using above-mentioned meteorological element message data error correcting method, above-mentioned mist Occurrence forecast method
Landscape forecast method, for playing the probability S of report day measuring and calculating prediction day generation sunrise landscape, it is characterised in that：According to following steps
Implement：
Step S1, mist probability of happening F of the prediction day at 08 is calculated08
The mist probability of happening F for predicting that day is secondary at 08 is calculated using above-mentioned mist Occurrence forecast method08, pre- give the correct time time be
Weather forecast 08 when time；
Step S2, the statistical probability E that sunrise landscape occurs under the influence of calculating total amount of cloud
Step S21, prediction day total amount of cloud correction value TC is calculated；
Total amount of cloud correction value TC secondary when predicting day 08 is calculated using above-mentioned meteorological element message data error correcting method,
The meteorological element is total amount of cloud, the meteorological telegraphic messages data AyIt is that meteorological observatory is secondary when playing the prediction day of report day issue 08
Message data, the i=5；
Step S22, total amount of cloud statistics minimum value TC is determinedzmin
Point moon statistics has sunrise landscape that total amount of cloud fact observation TC of the day at 08 occurs in nearly 5 yearsz08, obtain monthly
Count minimum value TCzmin；
Step S23, the statistical probability E that sunrise landscape occurs under the influence of total amount of cloud is determined
Fogless and total amount of cloud fact observation TC when 08 when daily 08 in nearly 5 years of point moon statisticsz08≥TCzminNumber of days；Root
According to TCz08Demarcation interval, it is determined that monthly each TCz08The number of days for having sunrise landscape to occur in section, obtains sunrise under the influence of total amount of cloud
The statistical probability E that landscape occurs；
Step S3, prediction day sunrise landscape probability of happening S is judged
Judge prediction day sunrise landscape probability of happening S according to following manner：
If predict day mist probability of happening F08The statistics minimum value of the moon where=0% and total amount of cloud correction value TC ＜ prediction days
TCzmin, judge sunrise landscape probability of happening S=100%；
If predicting day total amount of cloud correction value TC >=10, sunrise landscape probability of happening S=0% is judged；
If predict day mist probability of happening F08≠ the 0% and statistics minimum value TC of place moon prediction dayzminThe amendment of≤total amount of cloud
Value TC ＜ 10, sunrise landscape probability of happening S is calculated according to formula 3：
S=St×(1-F08) formula 3
In formula, S --- prediction day sunrise landscape probability of happening,
St--- statistical probability occurs for sunrise landscape, and sunrise landscape occurs under the influence of taking prediction day of that month corresponding total amount of cloud
Statistical probability E,
F08--- the probability of happening of prediction day mist, step S15 are determined.
Above-mentioned sunrise landscape forecast method is for playing the probability S of report day measuring and calculating prediction same day day generation sunrise landscape
Method.Whether sunrise landscape occurs to be influenceed by two factors, first, whether the sunrise period has mist, second, in sunrise period total amount of cloud
Size.Sunrise landscape forecast method of the present invention is playing the prediction of report day first with the above-mentioned mist Occurrence forecast method prediction of the present invention
Predict the mist probability of happening F of 08 period while being the 08 of weather forecast (give the correct time in advance time secondary) of day08.Secondly calculating total amount of cloud influences
The statistical probability E that lower sunrise landscape occurs；When predicting that total amount of cloud influences, with using the above-mentioned meteorological element message data of the present invention
During prediction day 08 that error correcting method is calculated based on secondary total amount of cloud correction value TC.Finally, mist hair is being considered jointly
Raw probability F08Judge sunrise landscape probability of happening S in the case of being influenceed with total amount of cloud correction value TC.Usually, Forecasting Methodology uses
Data basis be issue when reporting day 08 or 20 time prediction day weather forecast data, can so make this method with it is meteorological
The rule of platform issue weather forecast product is mutually connected, Effectual Utilization of Meteorological platform to public forecast model products, improve the applicability of method.
Based on above-mentioned meteorological element message data error correcting method, mist Occurrence forecast method, the present invention provides one
Kind sea of clouds landscape forecast method, its technical scheme are as follows：
It is a kind of to be realized using above-mentioned meteorological element message data error correcting method, above-mentioned mist Occurrence forecast method
Sea of clouds landscape forecast method, it is described pre- to give the correct time for time occurring the probability R of sea of clouds landscape when playing the measuring and calculating prediction daily forecast of report day
Secondary when being weather forecast times；It is characterized in that：Implement according to following steps：
Step S1, prediction day is calculated in the secondary mist probability of happening M that gives the correct time in advance
Prediction day is calculated in the mist probability of happening M to give the correct time in advance time using above-mentioned mist Occurrence forecast method, it is described pre- to give the correct time time
It is secondary when being weather forecast；
Step S2, the statistical probability N that sea of clouds landscape occurs under the influence of time low cloud cover during CALCULATING PREDICTION
Time low cloud cover correction value LC when step S21, calculating prediction daily forecast；
Low cloud cover correction value LC secondary when predicting daily forecast, institute are calculated using above-mentioned prediction of various weather constituents error correcting method
It is low cloud cover, the meteorological telegraphic messages data A to state meteorological elementyMeteorological observatory rise prediction day of report day issue give the correct time in advance it is secondary
Message data, the i=5；
Step S3, sea of clouds landscape probability of happening R secondary during prediction daily forecast is judged
If time low cloud cover correction value LC=0, judges sea of clouds landscape probability of happening value R=0% when predicting daily forecast,
If time low cloud cover correction value LC ＞ 0, judge sea of clouds landscape probability of happening value R=1-M when predicting daily forecast.
Above-mentioned sea of clouds landscape forecast method is that the general of sea of clouds landscape occurs for secondary when playing the measuring and calculating prediction daily forecast of report day
Rate R.Whether sea of clouds landscape is to be influenceed first by low cloud cover, therefore the feelings that the low cloud cover secondary when predicting daily forecast is zero
Condition, it can directly determine that sea of clouds landscape probability of happening is zero.After excluding sea of clouds landscape zero probability, sea of clouds landscape probability of happening is then
Mainly by whether thering is mist (mist probability of happening) to be influenceed.
Pre- in above-mentioned sea of clouds landscape forecast method is given the correct time when time being usually any weather forecast of prediction day time, can only be obtained
Must this when time each prediction of various weather constituents value time sea of clouds probability when can complete this measuring and calculating.Usually, for prediction day whole day
Sea of clouds landscape Occurrence forecast can design at the 08 of three independent periods, i.e. weather forecast time, 14 when time, 20 when time,
Namely complete to be directed to the morning, noon, three time points of dusk of one day to be predicted, while the meteorology also made with meteorological observatory
Forecast model products are engaged, convenient to obtain effective meteorological element data, improve Forecasting Methodology applicability.
The grand degree of sea of clouds landscape further can determine whether according to low cloud cover correction value：If 8 ＜ LC≤10, be judged as 1 grade it is non-
If Chang Zhuanguan seas of clouds, 5 ＜ LC≤8, if being judged as 2 grades of grand seas of clouds, 2 ＜ LC≤5, if being judged as 3 grades of a small amount of seas of clouds, 0 ＜ LC
≤ 2, it is judged as 4 grades of micro seas of clouds.
Based on above-mentioned meteorological element message data error correcting method, mist Occurrence forecast method, the present invention provides one
Kind rime landscape forecast method, its technical scheme are as follows：
A kind of mist realized using above-mentioned meteorological element message data error correcting method, above-mentioned mist Occurrence forecast method
Rime landscape forecast method, for playing the probability P of report day measuring and calculating prediction day generation rime landscape, it is characterised in that：The prediction
Day is the following 24h of described report day；Implement according to following steps：
Step S1, rime landscape probability of happening P is judged according to the period
Step S11, rime landscape time of origin section is determined
According to the rime landscape record data in predictably nearly 5 years, when statistics determines predictably annual rime landscape generation
Between section t；
Step S12, rime landscape probability of happening P is judged according to the period
Day not within the t periods, rime landscape probability of happening P=0 is judged, otherwise into step S2 if predicting；
Step S2, prediction day T is calculatedmin, temperature corrected value, Tavg
Step S21, prediction day lowest temperature correction value T is calculatedmin
Obtain message data Ay, the meteorological telegraphic messages data AyIt is meteorological observatory's temperature report in the following 24h for playing the issue of report day
Literary data；Recorded message data temperature minimum value is minimum temperature message data Tymin；
Prediction Daily minimum temperature message data T is calculated using above-mentioned prediction of various weather constituents error correcting methodyminIt is minimum
Warm correction value Tmin；
Step S22, prediction degree/day correction value, T are calculatedavg
Calculate temperature corrected value of the prediction day when different time respectively using above-mentioned prediction of various weather constituents error correcting method
T, the meteorological element are temperature, the meteorological telegraphic messages data AyIt is that meteorological observatory is being risen in the following 24h issued report day in difference
When time temperature message data, the i=5；
Secondary temperature corrected value T average value is recorded as predicting mean daily temperature T when will be differentavg；
Step S3, statistics minimum temperature critical value T is determinedzmin, mean temperature critical value Tzavg, rime landscape counts
Probability P '1、P′2
Divide the fact observation minimum temperature T ' of rime landscape process every time in moon statistics t periods of nearly 5 yearszmin, it is average
Temperature T 'zavg, monthly interior minimum temperature peak will be designated as working as monthly minimum temperature critical value Tzmin, will be per monthly mean temperature highest
Value is designated as working as monthly mean temperature critical value Tzavg；
According to the live observation mean temperature T ' of each rime landscape processzavgDemarcation interval, it is determined that monthly each T 'zavgSection
The number of days for inside thering is rime landscape to occur, obtain rime landscape and statistical probability P ' occurs1；
According to the live observation minimum temperature T ' of each rime landscape processzminDemarcation interval, it is determined that monthly each T 'zminSection
The number of days for inside thering is rime landscape to occur, obtain rime landscape and statistical probability P ' occurs2；
Step S4, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
If predict Daily minimum temperature TminMore than place moon Tzmin, rime landscape probability of happening P=0 is judged, otherwise, if rising
Report day that rime landscape occurs, if day rime landscape does not occur into step S5, a report, into step S6；
Step S5, according to prediction mean daily temperature TavgJudge rime landscape probability of happening P
If predict mean daily temperature TavgMore than place moon Tzavg, judge rime landscape probability of happening P=0, otherwise rime scape
It is T to see probability of happening PavgThe T ' at placezavgStatistical probability P ' occurs for rime corresponding to section1；
Step S6, rime landscape probability of happening P is judged according to prediction day mist probability of happening
Step S61, prediction day mist probability of happening is calculated
The probability that secondary mist occurs when calculating prediction day difference respectively using above-mentioned mist Occurrence forecast method, records mist
Maximum probability value is Fmax；
Step S62, rime landscape probability of happening P is judged according to prediction day mist probability of happening
If predicting, time mist probability of happening is 0 during day difference, rime landscape probability of happening P=0 is judged, otherwise into step
S7；
Step S7, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
The live observation Daily minimum temperature of rime landscape monthly occurs in the t periods in nearly 5 years of point moon statistics for the first time
T″zminIf predict Daily minimum temperature TminMoon T where ＞ "zmin, judge rime landscape probability of happening P=0, otherwise calculated according to formula 4
Determine rime landscape probability of happening P：
P=Fmax× K formulas 4
In formula, K-prediction day TminThe T ' at placezminStatistical probability P ' occurs for rime corresponding to section2。
Above-mentioned rime landscape forecast method, for rime to occur playing a report day measuring and calculating prediction day the following 24h of day (rise report)
The probability S of landscape.Whether the generation of rime landscape is by temperature (especially samming and lowest temperature) with having two factors of mist to be influenceed.On
Stating rime landscape uses the analytic statistics from historical data to obtain the statistical probability that temperature factor influence mist pine landscape occurs, then together
When consider mist probability of happening, thus obtain rime landscape probability of happening.It is engaged for the weather forecast product effectively with meteorological observatory,
In this method step S2, when considering temperature factor, temperature profile conduct secondary at 02,08,14,20 4 can be related to simultaneously
Whole day temperature index, i.e., using the mean temperature of at 02,08,14,20 4 times as whole day mean temperature.In step S6, considering
, it is necessary to be related to mist probability of happening secondary at 08,14,20 3 simultaneously as whole day mist probability of happening index when mist influences.
Compared with prior art, the beneficial effects of the invention are as follows：(1) the invention provides one kind to play report day to meteorological observatory
The method that secondary meteorological element message data is modified during the prediction daily forecast issued to public affairs, gained correction value are observed with live
Value is more closely, prediction error is smaller.Method can solve the problem that due to meteorological observatory the meteorological element predicted value sent can only with compared with
Based on the meteorological change modeling in big region, the environmental condition in some areas can not be utilized to adjust model calculation result,
The problem of meteorological observatory's predicted value that some areas caused by thus receive and larger live observation error.(2) present invention provides
Four kinds of mist Occurrence forecast method, sunrise landscape forecast method, sea of clouds landscape forecast method, rime landscape forecast method meteorological scapes
See Forecasting Methodology.(3) four kinds of meteorological diversity scenery Forecasting Methodologies provided by the invention are all with weather forecast of the meteorological observatory to public affairs issue
Product is data basis, is to be realized using meteorological observatory for the weather forecast product that big region is made to set small regional meteorology
The measuring and calculating of landscape probability of happening, public service data can be effectively utilized, had compared with high practicability.
Embodiment
With reference to preferred embodiment, technical solution of the present invention will be further described.
Embodiment one
1st group：Rise 2017041020 (during 10 days 20 April in 2017, similarly hereinafter) of report, forecast 20,170,411 08
With meteorological element message data error correcting method of the present invention to Mount Emei of Sichuan Province scenic spot on April 11st, 2017
Relative humidity meteorological observatory predicted value when 08 is modified.
Meteorological element A is relative humidity RH, plays report April 10 2017 day (specifically at 20), predicts in April, 2017 day
11 days, it is pre- give the correct time time 08 when.Relative humidity RH amendments during 11 days 08 April in 2017 are calculated i.e. at 10 days 20 April in 2017
Value.
According to history message data amount, using 5 days forward reporting 24h a few days ago from (the number of days i=5 for being used for error correction)
Relative humidity fact observation be used for this error correction.
Meteorological element message data RH in the number of days of error correctionyAnd corresponding live observation RHzData are shown in Table 1.1, according to formula
2 calculate each pair RHyWith RHzError delta RHi, Δ RH be calculated be shown in Table 1.1.
RH in the number of days of the error correction of table 1.1y、RHz、ΔRHi、ΔRH
The relative humidity message data that meteorological observatory is 12 hours to the Time effect forecast of public affairs issue during 10 days 20 April in 2017
RHy=98.8.
By RHy=98.8, Δ RH=1.5 substitutes into formula 1, (prediction day) relative humidity amendment when having 11 days 08 April in 2017
Value RH=97.3.
Result verification：Live observation during 11 days 08 April in 2017 is 97.9, and correction value is closer to live observation.
2nd group：Play report 2017021820, forecast 2017021908
Equally Mount Emei of Sichuan Province scenic spot rh value predicted value is modified.
Using with the 1st group of identical computational methods：According to rise give the correct time time be 2017 2 months 18 days 20 when message data exist
Predicted value RH at 2017 2 months 19 days 08y=95.3, Δ RH=-1.9 is calculated, is obtained on 2 19th, 2017 according to formula 1
Relative humidity correction value RH=97.2 when 08.
Result verification：Live observation at 2017 2 months 19 days 08 is RH=96.9, and correction value is observed closer to live
Value.
Embodiment two
1st group：Play report 2017041020, forecast：2017041108
With mist Occurrence forecast method of the present invention given the correct time from playing report 10 days April in 2017 of day times 20 when predict the high eyebrow in Sichuan Province
Mist probability of happening F when mountain scene area is when predicting day 11 daily forecast in April in 2017 secondary 08.
Step S1, relative humidity correction value RH secondary during prediction daily forecast is calculated
Calculated according to the 1st group of completion of embodiment 1, relative humidity correction value RH=97.3 during 11 days 08 April in 2017.
Step S2, statistics minimum value RH is determinedzmin
(give the correct time in advance time) has mist that relative humidity fact observation of the day at 08 occurs when daily 08 in nearly 5 years of point moon statistics
RHz, obtain monthly 08 when count minimum value RHzmin, it is shown in Table 2.1.
Table 2.1 nearly 5 years monthly 08,14 when relative humidity minimum value RH when having a mist generationzmin
| Month | RHzmin(when 08) | RHzmin(when 14) |
| 1 | 87 | 93 |
| 2 | 90 | 89 |
| 3 | 85 | 85 |
| 4 | 96 | 89 |
| 5 | 94 | 90 |
| 6 | 96 | 88 |
| 7 | 98 | 90 |
| 8 | 97 | 89 |
| 9 | 91 | 85 |
| 10 | 98 | 94 |
| 11 | 86 | 89 |
| 12 | 87 | 88 |
Step S3, determine that statistical probability F ' occurs for mist
The relative humidity fact observation RH of (give the correct time in advance time) during nearly 5 years daily 08 of point moon statisticszReach RHzminNumber of days；
According to RHzDivision statistics section, it is determined that monthly each RHzThe number of days for thering is mist to occur in section, obtain the monthly pre- time mist that gives the correct time and occur
Statistical probability F '.It the results are shown in Table 2.2.
Each shelves relative humidity mists the probability of appearance during table 2.2 08
| Month | 81≤RHz≤85 | 85 ＜ RHz≤90 | 90 ＜ RHz≤95 | 95 ＜ RHz≤99 | RHz＞ 99 |
| 1 | 0.0% | 66.7% | 72.0% | 62.5% | 75.0% |
| 2 | 0.0% | 40.0% | 66.7% | 88.2% | 72.7% |
| 3 | 25.0% | 25.0% | 37.5% | 67.2% | 74.5% |
| 4 | 0.0% | 0.0% | 0.0% | 41.9% | 72.9% |
| 5 | 0.0% | 0.0% | 19.4% | 0.0% | 94.7% |
| 6 | 0.0% | 0.0% | 8.3% | 78.9% | 82.6% |
| 7 | 0.0% | 0.0% | 9.1% | 68% | 73.1% |
| 8 | 0.0% | 0.0% | 0.0% | 57.4% | 77.8% |
| 9 | 0.0% | 0.0% | 100.0% | 64.3% | 80.8% |
| 10 | 0.0% | 0.0% | 0.0% | 67.4% | 79.7% |
| 11 | 0.0% | 20.0% | 65.7% | 82.3% | 100.0% |
| 12 | 0.0% | 20.0% | 65.7% | 82.3% | 100.0% |
Step S4, live observation minimum value RH ' is determinedzmin
(being given the correct time in advance secondary) when daily 08 in inquiry on April 10th, 2017 (playing report day) is first 30 days has mist that day occurs at 08
The relative humidity fact observation RH of (giving the correct time in advance secondary)zMinimum value RH 'zmin, record daily minimum value.Recorded according to Query Result
Nearly 5 days, it the results are shown in Table 2.3.
Live observation relative humidity minimum value RH ' when having mist generation during table 2.3 08zmin
| Sequence number | Date | RH′zmin |
| 1 | 20170408 | 98 |
| 2 | 20170407 | 96 |
| 3 | 20170328 | 90 |
| 4 | 20170326 | 95 |
| 5 | 20170321 | 88 |
Step S5, prediction day mist probability of happening F is judged according to following manner
There is the relative humidity minimum value RH during mist generation during April 08 countedzminIt is 96, has mist hair at nearest 5 days 08
The relative humidity minimum value RH ' of live observation when rawzminFor 88.
Relative humidity correction value RH=97.3 during 11 days 08 April in 2017, more than RHzmin=96 and RH 'zmin=88
Smaller, therefore the mist probability of happening F that (given the correct time in advance secondary) during on April 11st, 2017 (prediction day) 08 is April RH (=97.3) location
Between 95 ＜ RHzStatistical probability F '=41.9% occurs for≤99 mist.Mist possibility occurrence is smaller.
Result verification：By actual observation during 11 days 08 April in 2017, there is not mist in the moment, with prediction result
It coincide.
2nd group：Play report 2017021820, forecast 2017021908
Calculated according to the 2nd group of completion of embodiment 1, relative humidity correction value RH=97.2 at 2017 2 months 19 days 08.
Using with the 1st group of identical computational methods, when determining 2017 2 months 19 days 08 mist probability of happening F be 2 months RH (=
97.2) the ＜ RH of section 95 wherezStatistical probability F '=88.2% occurs for≤99 mist.Mist possibility occurrence is larger.
Result verification：There is mist at 2017 2 months 19 days 14, matched with predicted value.
Embodiment three
1st group：Play report 2017041020, forecast 2017041108
The sunrise scape in prediction Mount Emei of Sichuan Province scenic spot on April 11st, 2017 is predicted with sunrise landscape forecast method of the present invention
See probability of happening S.
Step S1, mist probability of happening F of the prediction day at 08 is calculated08
According to the 1st group of result of calculation of embodiment 2, mist hair of the Mount Emei scenic spot at 11 days 08 April in 2017 can be predicted
Raw probability F08=41.9%.
Step S2, the statistical probability E that sunrise landscape occurs under the influence of calculating total amount of cloud
Step S21, prediction day total amount of cloud correction value TC is calculated
Total amount of cloud when calculating 11 days 08 April in 2017 using meteorological element message data error correcting method of the present invention is repaiied
On the occasion of TC.The TC that modification is related in calculatingy、TCz、ΔTCi, Δ TC, TC be shown in Table
3.1.TC, TC in the number of days of the error correction of table 3.1z、ΔTCi, Δ TC, TC (i=5)
(Time effect forecast is 12 hours) total amount of cloud is during 11 days 08 April in 2017 issued during 10 days 20 April in 2017
6.9, the correction value TC=6.9-1.2=5.7 of total amount of cloud (total amount of cloud actual observed value during 11 days 08 April in 2017 is 6.0).
Step S22, total amount of cloud statistics minimum value TC is determinedzmin
Point moon statistics has sunrise landscape that total amount of cloud fact observation TC of the day at 08 occurs in nearly 5 yearsz08, obtain monthly
Count minimum value TCzmin.It is shown in Table 3.2
Table 3.2 has total amount of cloud minimum value TC at sunrisezmin
| Month | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 | 9 | 10 | 11 | 12 |
| TCzmin | 5.6 | 5.2 | 4.7 | 4.0 | 4.2 | 4.5 | 3.9 | 6.2 | 3.6 | 3.7 | 3.1 | 3.5 |
Step S23, the statistical probability E that sunrise landscape occurs under the influence of total amount of cloud is determined
Fogless and total amount of cloud fact observation TC when 08 when daily 08 in nearly 5 years of point moon statisticsz08≥TCzminNumber of days；Root
According to TCz08Demarcation interval, it is determined that monthly each TCz08The number of days for having sunrise landscape to occur in section, obtains sunrise under the influence of total amount of cloud
The statistical probability E that landscape occurs.It is shown in Table 3.3.
The total amount of cloud of table 3.3 and sunrise statistical probability E relation
| Month | 0 ＜ TCz08≤3 | 3 ＜ TCz08≤6 | 6 ＜ TCz08≤8 | 8 ＜ TCz08≤10 |
| 1 | 100.0% | 87.5% | 0.0% | 50.0% |
| 2 | 100.0% | 100.0% | 50.0% | 50.0% |
| 3 | 88.9% | 60.0% | 60.0% | 28.6% |
| 4 | 100.0% | 91.7% | 33.3% | 33.3% |
| 5 | 100.0% | 80.0% | 44.4% | 33.3% |
| 6 | 100.0% | 100.0% | 33.3% | 38.8% |
| 7 | 100.0% | 100.0% | 66.7% | 38.1% |
| 8 | 94.1% | 92.9% | 70.6% | 14.3% |
| 9 | 100.0% | 60.0% | 50.0% | 36.4% |
| 10 | 100.0% | 85.7% | 50.0% | 30.0% |
| 11 | 100.0% | 75.0% | 75.0% | 50.0% |
| 12 | 100.0% | 66.7% | 80.0% | 0.0% |
Step S3, prediction day sunrise landscape probability of happening S is judged
Step S1 calculates mist probability of happening F when determining 11 days 08 April in 201708=41.9%, step S21, which is calculated, determines always
Cloud amount correction value TC=5.7, statistics minimum value TC in Aprilzmin=4.0, i.e. mist probability of happening F08≠ 0% and statistics minimum value
TCzmin≤ total amount of cloud correction value TC ＜ 10, sunrise landscape probability of happening S is calculated according to formula 3.
Total amount of cloud correction value TC=5.7 is located at section 3~6, the statistical probability E=that corresponding sunrise landscape occurs
91.7%, i.e. St=91.7%, the probability of happening F of mist08=41.9%, therefore have sunrise landscape probability of happening S=0.917* according to formula 3
(1-0.419)=0.53.
Result verification：Verified through actual observation, morning on April 11st, 2017 has sunrise landscape, is relatively kissed with prediction result
Close.
2nd group：Play report 2017021820, forecast 2017021908
According to the 2nd group of result of calculation of embodiment 2, mist hair of the Mount Emei scenic spot at 2017 2 months 19 days 08 can be predicted
Raw probability F08=88.2%.
Using with the 1st group of identical computational methods, determine the correction value TC=6.5 of total amount of cloud at 2017 2 months 19 days 08,
When total amount of cloud predicted value at issued at 2017 2 months 18 days 20 2017 2 months 19 days 08 is 6.1,2017 2 months 19 days 08
Total amount of cloud actual observed value be 6.8).
2 months statistics minimum value TCzmin=5.2, i.e. mist probability of happening F08≠ 0% and statistics minimum value TCzmin≤ total amount of cloud
Correction value TC ＜ 10, sunrise landscape probability of happening S is calculated according to formula 3.
Total amount of cloud correction value TC=6.5 is located at section 6~8, the statistical probability E=that corresponding sunrise landscape occurs
50.0%, i.e. St=50.0%, the probability of happening F of mist08=88.2%, therefore have sunrise landscape probability of happening S=0.5* according to formula 3
(1-0.882)=0.06
Result verification：Verified through actual observation, sunrise landscape do not occur in 2 months 19 mornings in 2017, with prediction result
Relatively it coincide.
Example IV
1st group：Play report 2017041020, forecast 2017041108
With cloud during 11 days 08 April in 2017 of sea of clouds landscape forecast method of the present invention prediction prediction Mount Emei of Sichuan Province scenic spot
Seascape sees probability of happening R.
Step S1, prediction day is calculated in the secondary mist probability of happening M that gives the correct time in advance
According to the 1st group of result of calculation of embodiment 2, mist probability of happening M during Mount Emei scenic spot 11 days 08 April in 2017 is
=41.9%.
Step S2, the statistical probability N that sea of clouds landscape occurs under the influence of calculating prediction time low cloud cover
Time low cloud cover correction value LC when step S21, calculating prediction daily forecast
Low cloud cover when calculating 11 days 08 April in 2017 using meteorological element message data error correcting method of the present invention is repaiied
On the occasion of LC.The LC that modification is related in calculatingy、LCz、ΔLCi, Δ LC, LC be shown in Table 4.1.
LC in the number of days of the error correction of table 4.1y、LCz、ΔLCi, Δ LC, LC (i=5)
During 10 days 20 April in 2017 during 11 days 08 April in 2017 of (rise give the correct time time) issue (during prediction time, Time effect forecast
For 12 hours) low cloud cover is 10.Low cloud cover correction value LC=10.0-1.1=8.9 is calculated (during 11 days 08 April in 2017
8.1) low cloud cover actual observed value is.
Step S3, sea of clouds landscape probability of happening R secondary during prediction daily forecast is judged
Step S1 determines that mist probability of happening M=41.9%, step S21 determine low cloud cover amendment during 11 days 08 April in 2017
Value LC=8.9 ＞ 8, judge sea of clouds landscape probability of happening R=1-M=1-41.9%=58.1% according to formula, therefore it is possible that sea of clouds occurs
Property is larger, and is 1 grade of very grand sea of clouds.
Result verification：There is more grand sea of clouds landscape by actual observation, during 11 days 08 April in 2017, it is and pre-
Result is surveyed to match.
2nd group：Play report 2017021820, forecast 2017021908
According to rise give the correct time time be 2017 2 months 18 days 20 when message data, the low cloud cover at 2017 2 months 19 days 14
Message data LCy=7.8, determine Δ LC=1.4 using being calculated with the 1st group of same procedure, then when having 2017 2 months 19 days 14
Low cloud cover correction value LC=6.4 (low cloud cover actual observed value at 2017 2 months 19 days 08 is 6.7).
Mist probability of happening M=88.2%, step S21 determine low clouds when the 2nd group of embodiment 3 determines 2017 2 months 19 days 08
Correction value LC=6.4 ＞ 0 are measured, judge sea of clouds landscape probability of happening R=1-M=1-88.2=11.8% according to formula, therefore sea of clouds occurs
Possibility very little.
Result verification：There is not sea of clouds landscape at 2017 2 months 19 days 08, more coincide with predicted value.
Embodiment five
Mount Emei of Sichuan Province scenic spot is calculated respectively April 11,2017 in 2017 with rime landscape forecast method of the present invention
The rime landscape probability of happening P in two days on the 19th 2 months year.
1st group：Play report 2017041020, forecast 20170411
Step S1, rime landscape probability of happening P is judged according to the period
Step S11, rime landscape time of origin section is determined
According to the rime landscape record data in nearly 5 years of Mount Emei scenic spot, statistics determines the annual rime scape in Mount Emei scenic spot
See time of origin section t 1,2,3,4,11, December.
Step S12, rime landscape probability of happening P is judged according to the period
Prediction belongs to the t periods on April 11 2017 day, into step S2.
Step S2, prediction degree/day correction value, T are calculatedmin、Tavg
Step S21, prediction day lowest temperature correction value T is calculatedmin
Using message data AyIt is (the temperature of temperature prediction value in following 24h that meteorological observatory issued at 10 days 20 April in 2017
Spend message data Ty).First, the minimum temperature predicted value of prediction day is obtained, correlation computations data are shown in Table 5.1.
The temperature message data T of table 5.1y
Minimum temperature message data T in the following 24h issued during 10 days 20 April in 2017ymin=2.96.Next profit
Minimum temperature predicted value is modified with error correcting method, obtains minimum temperature correction value Tmin。
It is pre- to give the correct time when time be 11 days 08 April in 2017, act secondary when being 10 days 20 April in 2017, the Time effect forecast 12 that gives the correct time
Hour, the T being related in corrected Calculationy、Tz、ΔTi、ΔT、TminIt is shown in Table 5.2.
T in the number of days of the error correction of table 5.2y、Tz、ΔTi、ΔT、Tmin(i=5)
Predict the minimum temperature correction value T in April 11 2017 daymin=2.49.
Step S22, prediction degree/day correction value, T are calculatedavg
When calculating on April 11st, 2017 respectively at 02,08 using prediction of various weather constituents error correcting method of the present invention, 14
When, 20 when four when time temperature corrected value T02、T08、T14、T20, using message data AyIt is meteorological observatory on April 10th, 2017
When in the following 24h that is issued when 20 at 02,08,14 when, 20 when four when time message data.According to error correcting method pair
Secondary temperature prediction value is modified at this four, obtains temperature corrected value T02=2.93, T08=3.12, T14=3.35, T20=
3.25。
By T02、T08、T14、T20Average value be recorded as predicting 11 mean daily temperature T in April 2017 dayavg=3.16.
Step S3, statistics minimum temperature critical value T is determinedzmin, mean temperature highest critical value Tzavg, rime landscape occur
Statistical probability P '1、P′2
Nearly 5 years of point moon statistics 1,2,3,4,11, the live observation of rime landscape process every time in December (in the t periods)
Minimum temperature T 'zmin, mean temperature T 'zavg, monthly interior minimum temperature peak will be designated as working as monthly minimum temperature critical value Tzmin、
Monthly interior mean temperature peak it will be designated as working as monthly mean temperature critical value Tzavg.It the results are shown in Table 5.3.
The rime landscape temperature threshold Data-Statistics of table 53
According to each rime landscape process mean temperature T 'zavgDemarcation interval, it is determined that monthly each T 'zavgThere is rime in section
The number of days that landscape occurs, obtain rime landscape and statistical probability P ' occurs1；According to each rime landscape process minimum temperature T 'zminDraw
By stages, it is determined that monthly each T 'zminThe number of days for thering is rime landscape to occur in section, obtain rime landscape and statistical probability P ' occurs2。
It the results are shown in Table 5.4.
The relation of the minimum temperature of table 5.4 and mean temperature and rime
Step S4, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
Predict Daily minimum temperature T in April 2017 day 11min=2.49≤place moon Tzmin=5.2, rise and report in April, 2017 day
Rime landscape occurred in 10th, hereafter calculate and enter step S5.
Step S5, according to prediction mean daily temperature TavgJudge rime landscape probability of happening P
Predict mean daily temperature correction value T in April 2017 day 11avg=3.16≤place moon Tzavg=9.5, judge rime
Landscape probability of happening P is TavgThe T ' at placezavgStatistical probability P ' occurs for rime corresponding to section1=0.4.Rime probability of happening is inclined
It is small.
Result verification：Through actual observation, on April 11st, 2017 does not occur rime.
2nd group：Play report 2017021820, forecast 20170219
Step S1 is the same as the 1st group.
Step S2, prediction degree/day correction value, T are calculatedmin、Tavg
Using with the 1st group of same computational methods, be calculated prediction at 19 on day 2017 year 2 month minimum temperature correction value
Tmin=-3.78.Be calculated prediction at 19 on day 2017 year 2 month at 02,08 when, 14 when, 20 when four when time forecast temperature
Correction value T02=-3.23, T08=-1.85, T14=8.24, T20=7.68, by T02、T08、T14、T20Average value be recorded as it is pre-
Survey the mean temperature T on day 2017 year 2 month 19avg=2.71.
Step S3 is the same as the 1st group.
Step S4, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
Predict 2 months days 2017 year, 19 Daily minimum temperature Tmin=-3.78≤place moon Tzmin=4.4, play 2017 2 day of report
Rime landscape did not occurred in 18 for the moon, hereafter calculates and enters step S6.
Step S6, rime landscape probability of happening P is judged according to prediction day mist probability of happening
Step S61, prediction day mist probability of happening is calculated
When calculating prediction day 2017 year 2 month 19 respectively using mist Occurrence forecast method of the present invention at 08 time, 14 time, 20
When time mist occur probability, obtain F08=0.4, F14=0.892, F20=0, maximum probability Fmax=0.892.
Step S62, rime landscape probability of happening P is judged according to prediction day mist probability of happening
Time mist probability of happening F during due to prediction day difference08、F14、F20Non- is 0, into step S7.
Step S7, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
Point moon statistics nearly 5 years in 1,2,3,4,11, December (in the t periods) monthly for the first time occur rime landscape mistake
The live observation Daily minimum temperature T " of journeyzmin.It the results are shown in Table 5.5.
Table 5.5 monthly first time rime process minimum temperature statistics T "zmin
| Month | 1 | 2 | 3 | 4 | 11 | 12 |
| T″zmin | -4.6 | -3.9 | -2.9 | -2.5 | -3.4 | -4.0 |
Predict 2 months days 2017 year, 19 Daily minimum temperature Tmin=-3.78≤2 months T "zmin=-3.9, predict day TminPlace
T′zminStatistical probability P ' occurs for rime corresponding to section2=0.8, then K=0.8, so judging rime landscape probability of happening P=
Fmax× K=0.892*0.8=0.714.Rime landscape probability of happening is higher.
Result verification：There is rime 19 afternoon through actual observation 2017 year 2 month.
Claims (10)
1. meteorological element message data error correcting method, for playing report day amendment meteorological observatory to the public meteorological telegraphic messages number issued
According to AyObtain predicting day meteorological element correction value A, it is characterised in that：Predict that day meteorological element correction value A calculates according to formula 1 to determine：
A=Ay- Δ A formulas 1
In formula, A --- prediction day meteorological element correction value,
Ay--- report day meteorological element message data is played,
Δ A --- error correction values, calculate and determine according to formula 2；
In formula, i --- it is used for the number of days of error correction reporting 24h a few days ago from forward, is determined according to history message data；
ΔAi--- in the number of days of error correction, each meteorological element message data AyWith corresponding live observation AzMistake
Difference.
2. according to the method for claim 1, it is characterised in that：The meteorological element be temperature, relative humidity, low cloud cover or
Total amount of cloud；The i=5.
3. the mist Occurrence forecast method realized using the meteorological element message data error correcting method described in claim 1, use
Secondary mist probability of happening F during Yu Qi report day measuring and calculating prediction daily forecasts, it is characterised in that：Implement according to following steps：
Step S1, relative humidity correction value RH secondary during prediction daily forecast is calculated；
Relative humidity secondary when predicting daily forecast is calculated using meteorological element message data error correcting method described in claim 1
Correction value RH, the meteorological element are relative humidity, the meteorological telegraphic messages data AyIt is that the prediction of report day issue is playing in meteorological observatory
Secondary message data, the i=5 during daily forecast；
Step S2, statistics minimum value RH is determinedzmin
Time there is mist that day occurs in the relative humidity fact observation RH to give the correct time in advance time when in nearly 5 years of point moon statistics per daily forecastz, obtain
To the monthly pre- time statistics minimum value RH that gives the correct timezmin；
Step S3, determine that statistical probability F ' occurs for mist
Secondary relative humidity fact observation RH when dividing moon statistics nearly 5 years per daily forecastzReach RHzminNumber of days；According to RHzDraw
Divide statistics section, it is determined that monthly each RHzThe number of days for having mist to occur in section, statistical probability occurs for time mist when obtaining every lunate tail
F′；
Step S4, live observation minimum value RH ' is determinedzmin
Having inquired about in report 30 days a few days ago time has mist that day occurs in the relative humidity fact observation RH to give the correct time in advance time during per daily forecastz
Minimum value RH 'zmin, record daily minimum value；The minimum value of nearly 5 times is recorded if intrinsic fog on the 30th occurs to be more than 5 times；
Step S5, prediction day mist probability of happening F is judged according to following manner
If the pre- time relative humidity correction value RH ＜ statistics minimum values RH that gives the correct time obtained by step S1zminMinimum value is observed with fact
RH′zminSmaller, judge to predict day in the mist probability of happening F=0% to give the correct time in advance time；
If the pre- time relative humidity correction value RH >=statistics minimum value RH that gives the correct time obtained by step S1zminMinimum value is observed with fact
RH′zminSmaller, judge to predict that day in the mist probability of happening F to give the correct time in advance time is moon RH where prediction dayzIt is corresponding to count section
Mist statistical probability F ' occurs.
4. mist Occurrence forecast method according to claim 3, it is characterised in that：It is predictably Mount Emei scenic spot；The step
In rapid S3, RHzStatistical probability F ' relations occur for time mist as follows when section is with per lunate tail：
Table 1
5. utilize the meteorological element message data error correcting method described in claim 1, the mist hair described in claim 3 or 4
The sunrise landscape forecast method that raw Forecasting Methodology is realized, for playing the probability S for report day calculating prediction day and sunrise landscape occurring, its
It is characterised by：Implement according to following steps：
Step S1, mist probability of happening F of the prediction day at 08 is calculated08
The mist probability of happening F for predicting that day is secondary at 08 is calculated using mist Occurrence forecast method described in claim 408, the forecast
When time being the 08 of weather forecast time；Step S2, the statistical probability E that sunrise landscape occurs under the influence of calculating total amount of cloud
Step S21, prediction day total amount of cloud correction value TC is calculated；
Total amount of cloud amendment secondary when predicting day 08 is calculated using meteorological element message data error correcting method described in claim 1
Value TC, the meteorological element are total amount of cloud, the meteorological telegraphic messages data AyIt is that the prediction day of report day issue is being played 08 by meteorological observatory
When time message data, the i=5；
Step S22, total amount of cloud statistics minimum value TC is determinedzmin
Point moon statistics has sunrise landscape that total amount of cloud fact observation TC of the day at 08 occurs in nearly 5 yearsz08, monthly counted
Minimum value TCzmin；
Step S23, the statistical probability E that sunrise landscape occurs under the influence of total amount of cloud is determined
Fogless and total amount of cloud fact observation TC when 08 when daily 08 in nearly 5 years of point moon statisticsz08≥TCzminNumber of days；According to
TCz08Demarcation interval, it is determined that monthly each TCz08The number of days for having sunrise landscape to occur in section, obtains sunrise scape under the influence of total amount of cloud
See the statistical probability E occurred；
Step S3, prediction day sunrise landscape probability of happening S is judged
Judge prediction day sunrise landscape probability of happening S according to following manner：
If predict day mist probability of happening F08The statistics minimum value TC of the moon where=0% and total amount of cloud correction value TC ＜ prediction dayszmin,
Judge sunrise landscape probability of happening S=100%；
If predicting day total amount of cloud correction value TC >=10, sunrise landscape probability of happening S=0% is judged；
If predict day mist probability of happening F08≠ the 0% and statistics minimum value TC of place moon prediction dayzmin≤ total amount of cloud correction value TC
＜ 10, sunrise landscape probability of happening S is calculated according to formula 3：
S=St×(1-F08) formula 3
In formula, S --- prediction day sunrise landscape probability of happening,
St--- statistical probability occurs for sunrise landscape, takes the system of sunrise landscape generation under the influence of prediction day of that month corresponding total amount of cloud
Probability E is counted,
F08--- the probability of happening of prediction day mist, step S15 are determined.
6. sunrise landscape forecast method according to claim 5, it is characterised in that：It is predictably Mount Emei scenic spot, it is described
In step S23, TCz08The statistical probability E relations that sunrise landscape occurs under the influence of total amount of cloud corresponding to section are as follows：
Table 2
7. utilize the meteorological element message data error correcting method described in claim 1, the mist hair described in claim 3 or 4
The sea of clouds landscape forecast method that raw Forecasting Methodology is realized, for time generation sea of clouds landscape when a report day calculating prediction daily forecast
Probability R, it is described pre- to give the correct time when time being weather forecast time；It is characterized in that：Implement according to following steps：
Step S1, prediction day is calculated in the secondary mist probability of happening M that gives the correct time in advance
Prediction day is calculated in the secondary mist probability of happening M that gives the correct time in advance, the forecast using mist Occurrence forecast method described in claim 4
When time being weather forecast time；
Step S2, the statistical probability N that sea of clouds landscape occurs under the influence of calculating prediction time low cloud cover
Time low cloud cover correction value LC when step S21, calculating prediction daily forecast；
Low cloud cover correction value secondary when predicting daily forecast is calculated using prediction of various weather constituents error correcting method described in claim 1
LC, the meteorological element are low cloud cover, the meteorological telegraphic messages data AyBeing meteorological observatory is playing giving the correct time in advance time for report day issue
Predict day message data, the i=5；
Step S3, sea of clouds landscape probability of happening R secondary during prediction daily forecast is judged
If time low cloud cover correction value LC=0, judges sea of clouds landscape probability of happening value R=0% when predicting daily forecast,
If time low cloud cover correction value LC ＞ 0, judge sea of clouds landscape probability of happening value R=1-M when predicting daily forecast.
8. utilize the meteorological element message data error correcting method described in claim 1, the mist hair described in claim 3 or 4
The rime landscape forecast method that raw Forecasting Methodology is realized, for playing probability P report day calculated prediction day and rime landscape occurs, its
It is characterised by：The prediction day is the following 24h of described report day；Implement according to following steps：
Step S1, rime landscape probability of happening P is judged according to the period
Step S11, rime landscape time of origin section is determined
According to the rime landscape record data in predictably nearly 5 years, statistics determines predictably annual rime landscape time of origin section
t；
Step S12, rime landscape probability of happening P is judged according to the period
Day not within the t periods, rime landscape probability of happening P=0 is judged, otherwise into step S2 if predicting；
Step S2, prediction day T is calculatedmin, temperature corrected value, Tavg
Step S21, prediction day lowest temperature correction value T is calculatedmin
Obtain message data Ay, the meteorological telegraphic messages data AyIt is that meteorological observatory is being risen in the following 24h issued report day in temperature message
Data；Recorded message data temperature minimum value is minimum temperature message data Tymin；
Prediction Daily minimum temperature message data T is calculated using prediction of various weather constituents error correcting method described in claim 1ymin's
Lowest temperature correction value Tmin；
Step S22, prediction degree/day correction value, T are calculatedavg
Temperature of the prediction day when different time is calculated respectively using prediction of various weather constituents error correcting method described in claim 1 to repair
On the occasion of T, the meteorological element is temperature, the meteorological telegraphic messages data AyIt is that meteorological observatory is being risen in the following 24h issued report day not
Secondary message data, the i=5 simultaneously；
Secondary temperature corrected value T average values are recorded as predicting mean daily temperature T when will be differentavg；
Step S3, statistics minimum temperature critical value T is determinedzmin, mean temperature critical value Tzavg, rime landscape occur statistical probability
P′1、P′2
Divide the fact observation minimum temperature T ' of rime landscape process every time in moon statistics t periods of nearly 5 yearszmin, mean temperature
T′zavg, monthly interior minimum temperature peak will be designated as working as monthly minimum temperature critical value Tzmin, by the note per monthly mean temperature peak
For as monthly mean temperature critical value Tzavg；
According to each rime landscape process mean temperature T 'zavgDemarcation interval, it is determined that monthly each T 'zavgThere is rime landscape in section
The number of days of generation, obtain rime landscape and statistical probability P ' occurs1；
According to each rime landscape process minimum temperature T 'zminDemarcation interval, it is determined that monthly each T 'zminThere is rime landscape in section
The number of days of generation, obtain rime landscape and statistical probability P ' occurs2；
Step S4, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
If predict Daily minimum temperature TminMore than place moon Tzmin, rime landscape probability of happening P=0 is judged, otherwise, if playing report day hair
Fog rime landscape, if day rime landscape does not occur into step S5, a report, into step S6；
Step S5, according to prediction mean daily temperature TavgJudge rime landscape probability of happening P
If predict mean daily temperature TavgMore than place moon Tzavg, judge rime landscape probability of happening P=0, otherwise rime landscape is sent out
Raw probability P is TavgThe T ' at placezavgStatistical probability P ' occurs for rime corresponding to section1；
Step S6, rime landscape probability of happening P is judged according to prediction day mist probability of happening
Step S61, prediction day mist probability of happening is calculated
The probability that secondary mist occurs when calculating prediction day difference respectively using mist Occurrence forecast method described in claim 4, record
Mist probability of happening maximum is Fmax；
Step S62, according to prediction day mist probability of happening F08、F14、F20Judge rime landscape probability of happening P
If predicting, time mist probability of happening is 0 during day difference, rime landscape probability of happening P=0 is judged, otherwise into step S7；
Step S7, according to prediction Daily minimum temperature TminJudge rime landscape probability of happening P
The live observation Daily minimum temperature T " of rime landscape monthly occurs in the t periods in nearly 5 years of point moon statistics for the first timezmin,
If predict Daily minimum temperature Tmin＞ T "zmin, judge rime landscape probability of happening P=0, otherwise calculated according to formula 4 and determine rime landscape
Probability of happening P：
P=Fmax× K formulas 4
In formula, K- prediction days TminThe T ' at placezminStatistical probability P ' occurs for rime corresponding to section2。
9. rime landscape forecast method according to claim 8, it is characterised in that：The meteorological telegraphic messages data AyIt is meteorological
Temperature message data in the following 24h that platform is issued when playing report day 20；In the step S22, when time being 02 when described different, 08
When, 14 when, 20 when；In the step S6, when time 08 when described different, 14 when, 20 when.
10. rime landscape forecast method according to claim 8, it is characterised in that：It is predictably Mount Emei scenic spot, it is described
In step S3, T 'zavgWith rime landscape statistical probability P ' occurs for section1Relation, T 'zminWith rime landscape statistics occurs for section generally
Rate P '2Relation is as follows：
Table 3
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710996151.0A CN107748933B (en) | 2017-10-23 | 2017-10-23 | Meteorological element message data error correction method and fog, sunrise, cloud sea and rime prediction method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710996151.0A CN107748933B (en) | 2017-10-23 | 2017-10-23 | Meteorological element message data error correction method and fog, sunrise, cloud sea and rime prediction method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107748933A true CN107748933A (en) | 2018-03-02 |
| CN107748933B CN107748933B (en) | 2020-07-14 |
Family
ID=61253012
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710996151.0A Active CN107748933B (en) | 2017-10-23 | 2017-10-23 | Meteorological element message data error correction method and fog, sunrise, cloud sea and rime prediction method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN107748933B (en) |
Cited By (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109670642A (en) * | 2018-12-18 | 2019-04-23 | 北京无线电测量研究所 | A kind of mist forecasting procedure, system, medium and equipment based on meteorological measuring |
| CN112218061A (en) * | 2020-11-16 | 2021-01-12 | 云南腾云信息产业有限公司 | Live broadcast reminding method, device, server and medium |
| CN113156546A (en) * | 2021-03-12 | 2021-07-23 | 重庆市气象台 | Sunrise and sunset landscape forecasting method and system |
| CN114244873A (en) * | 2022-02-28 | 2022-03-25 | 深圳市千百炼科技有限公司 | Distributed task scheduling-based GFS meteorological data distribution and transmission method |
| CN115508916A (en) * | 2022-10-17 | 2022-12-23 | 重庆市气象台 | Starry sky landscape forecasting method |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930356A (en) * | 2012-11-16 | 2013-02-13 | 广东电网公司电力调度控制中心 | Short-term load forecast method based on meteorological factor sensitivity |
| CN105095667A (en) * | 2015-08-18 | 2015-11-25 | 国家电网公司 | Method for computing maximum number of continuous ice-coating days based on glaze and rime identification |
| CN106408223A (en) * | 2016-11-30 | 2017-02-15 | 华北电力大学（保定） | Short-term load prediction based on meteorological similar day and error correction |
| US20170075035A1 (en) * | 2015-09-11 | 2017-03-16 | Kabushiki Kaisha Toshiba | Probabilistic weather forecasting device, probabilistic weather forecasting method, and non-transitory computer readable medium |
| CN106772697A (en) * | 2016-11-21 | 2017-05-31 | 元江哈尼族彝族傣族自治县气象局 | Sea of clouds natural landscape forecasting procedure and system |
-
2017
- 2017-10-23 CN CN201710996151.0A patent/CN107748933B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102930356A (en) * | 2012-11-16 | 2013-02-13 | 广东电网公司电力调度控制中心 | Short-term load forecast method based on meteorological factor sensitivity |
| CN105095667A (en) * | 2015-08-18 | 2015-11-25 | 国家电网公司 | Method for computing maximum number of continuous ice-coating days based on glaze and rime identification |
| US20170075035A1 (en) * | 2015-09-11 | 2017-03-16 | Kabushiki Kaisha Toshiba | Probabilistic weather forecasting device, probabilistic weather forecasting method, and non-transitory computer readable medium |
| CN106772697A (en) * | 2016-11-21 | 2017-05-31 | 元江哈尼族彝族傣族自治县气象局 | Sea of clouds natural landscape forecasting procedure and system |
| CN106408223A (en) * | 2016-11-30 | 2017-02-15 | 华北电力大学（保定） | Short-term load prediction based on meteorological similar day and error correction |
Cited By (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109670642A (en) * | 2018-12-18 | 2019-04-23 | 北京无线电测量研究所 | A kind of mist forecasting procedure, system, medium and equipment based on meteorological measuring |
| CN112218061A (en) * | 2020-11-16 | 2021-01-12 | 云南腾云信息产业有限公司 | Live broadcast reminding method, device, server and medium |
| CN112218061B (en) * | 2020-11-16 | 2023-04-07 | 云南腾云信息产业有限公司 | Live broadcast reminding method, device, server and medium |
| CN113156546A (en) * | 2021-03-12 | 2021-07-23 | 重庆市气象台 | Sunrise and sunset landscape forecasting method and system |
| CN113156546B (en) * | 2021-03-12 | 2023-02-17 | 重庆市气象台 | Sunrise and sunset landscape forecasting method and system |
| CN114244873A (en) * | 2022-02-28 | 2022-03-25 | 深圳市千百炼科技有限公司 | Distributed task scheduling-based GFS meteorological data distribution and transmission method |
| CN115508916A (en) * | 2022-10-17 | 2022-12-23 | 重庆市气象台 | Starry sky landscape forecasting method |
Also Published As
| Publication number | Publication date |
|---|---|
| CN107748933B (en) | 2020-07-14 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107748933A (en) | Meteorological element message data error correcting method, mist, sunrise, sea of clouds, rime Forecasting Methodology | |
| Xu et al. | Projected changes in temperature and precipitation extremes over China as measured by 50-yr return values and periods based on a CMIP5 ensemble | |
| Wan et al. | Development of an equivalent wind plant power-curve | |
| Hannah et al. | An approach to hydrograph classification | |
| Lutz et al. | Comparison of climate change signals in CMIP3 and CMIP5 multi-model ensembles and implications for Central Asian glaciers | |
| Hart et al. | An evaluation of mesoscale-model-based model output statistics (MOS) during the 2002 Olympic and Paralympic Winter Games | |
| Hakala et al. | Hydrological modeling to evaluate climate model simulations and their bias correction | |
| CN107067683B (en) | A kind of transmission line forest fire clusters quantitative forecast method and system | |
| Allen et al. | Evaluating changes in season length, onset, and end dates across the United States (1948–2012) | |
| CN110059915A (en) | A kind of winter wheat meteorological disaster integrated risk dynamic evaluation method and device | |
| CN112114384B (en) | Power transmission line icing occurrence probability forecasting method | |
| CN107765348A (en) | Precipitation meteorological telegraphic messages data error modification method, glaze landscape forecast method | |
| CN103683274A (en) | Regional long-term wind power generation capacity probability prediction method | |
| Gardner et al. | Influence of the Arctic circumpolar vortex on the mass balance of Canadian High Arctic glaciers | |
| Miao et al. | A fuzzy logic fog forecasting model for Perth Airport | |
| Sridevi et al. | Rainfall forecasting skill of GFS model at T1534 and T574 resolution over India during the monsoon season | |
| CN114442198A (en) | Forest fire weather grade forecasting method based on weighting algorithm | |
| CN111915083A (en) | Wind power prediction method and prediction system based on time hierarchical combination | |
| CN108734278A (en) | A kind of low visibility forecasting procedure based on the multiple neural network method of successive approximation | |
| Hart et al. | Model forecast improvements with decreased horizontal grid spacing over finescale intermountain orography during the 2002 Olympic Winter Games | |
| KR20160074325A (en) | Electricity Demand Index (EDI) Forecasting System with respect to Weather Condition Change | |
| CN114004514A (en) | Atmospheric ozone pollution occurrence meteorological condition grade discrimination method | |
| Li et al. | Spatio-temporal forecasting of weather-driven damage in a distribution system | |
| Cannon et al. | Determining the bounds of skilful forecast range for probabilistic prediction of system-wide wind power generation | |
| CN108154259A (en) | Load forecasting method and device, storage medium, the processor of heat pump |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |